Drake
MinimumValueConstraint Class Referencefinal

Detailed Description

Constrain all elements of the vector returned by the user-provided function to be no smaller than a specified minimum value.

The formulation of the constraint is

SmoothMax( φ((vᵢ - v_influence)/(v_influence - vₘᵢₙ)) / φ(-1) ) ≤ 1

where vᵢ is the i-th value returned by the user-provided function, vₘᵢₙ is the minimum allowable value, v_influence is the "influence value" (the value below which an element influences the constraint or, conversely, the value above which an element is ignored), φ is a solvers::MinimumValuePenaltyFunction, and SmoothMax(v) is a smooth, conservative approximation of max(v) (i.e. SmoothMax(v) >= max(v), for all v). We require that vₘᵢₙ < v_influence. The input scaling (vᵢ - v_influence)/(v_influence - vₘᵢₙ) ensures that at the boundary of the feasible set (when vᵢ == vₘᵢₙ), we evaluate the penalty function at -1, where it is required to have a non-zero gradient. The user-provided function may return a vector with up to max_num_values elements. If it returns a vector with fewer than max_num_values elements, the remaining elements are assumed to be greater than the "influence value".

#include <drake/solvers/minimum_value_constraint.h>

Public Member Functions

 MinimumValueConstraint (int num_vars, double minimum_value, double influence_value_offset, int max_num_values, std::function< AutoDiffVecXd(const Eigen::Ref< const AutoDiffVecXd > &, double)> value_function, std::function< VectorX< double >(const Eigen::Ref< const VectorX< double >> &, double)> value_function_double={})
 Constructs a MinimumValueConstraint. More...
 
 ~MinimumValueConstraint () override
 
double minimum_value () const
 Getter for the minimum value. More...
 
double influence_value () const
 Getter for the influence value. More...
 
double max_num_values () const
 Getter for maximum number of values expected from value_function. More...
 
void set_penalty_function (MinimumValuePenaltyFunction new_penalty_function)
 Setter for the penalty function. More...
 
- Public Member Functions inherited from Constraint
template<typename DerivedLB , typename DerivedUB >
 Constraint (int num_constraints, int num_vars, const Eigen::MatrixBase< DerivedLB > &lb, const Eigen::MatrixBase< DerivedUB > &ub, const std::string &description="")
 Constructs a constraint which has num_constraints rows, with an input num_vars x 1 vector. More...
 
 Constraint (int num_constraints, int num_vars)
 Constructs a constraint which has num_constraints rows, with an input num_vars x 1 vector, with no bounds. More...
 
bool CheckSatisfied (const Eigen::Ref< const Eigen::VectorXd > &x, double tol=1E-6) const
 Return whether this constraint is satisfied by the given value, x. More...
 
bool CheckSatisfied (const Eigen::Ref< const AutoDiffVecXd > &x, double tol=1E-6) const
 
symbolic::Formula CheckSatisfied (const Eigen::Ref< const VectorX< symbolic::Variable >> &x) const
 
const Eigen::VectorXd & lower_bound () const
 
const Eigen::VectorXd & upper_bound () const
 
int num_constraints () const
 Number of rows in the output constraint. More...
 
 Constraint (const Constraint &)=delete
 
Constraintoperator= (const Constraint &)=delete
 
 Constraint (Constraint &&)=delete
 
Constraintoperator= (Constraint &&)=delete
 
- Public Member Functions inherited from EvaluatorBase
virtual ~EvaluatorBase ()
 
void Eval (const Eigen::Ref< const Eigen::VectorXd > &x, Eigen::VectorXd *y) const
 Evaluates the expression. More...
 
void Eval (const Eigen::Ref< const AutoDiffVecXd > &x, AutoDiffVecXd *y) const
 Evaluates the expression. More...
 
void Eval (const Eigen::Ref< const VectorX< symbolic::Variable >> &x, VectorX< symbolic::Expression > *y) const
 Evaluates the expression. More...
 
void set_description (const std::string &description)
 Set a human-friendly description for the evaluator. More...
 
const std::string & get_description () const
 Getter for a human-friendly description for the evaluator. More...
 
std::ostream & Display (std::ostream &os, const VectorX< symbolic::Variable > &vars) const
 Formats this evaluator into the given stream using vars for the bound decision variable names. More...
 
std::ostream & Display (std::ostream &os) const
 Formats this evaluator into the given stream, without displaying the decision variables it is bound to. More...
 
int num_vars () const
 Getter for the number of variables, namely the number of rows in x, as used in Eval(x, y). More...
 
int num_outputs () const
 Getter for the number of outputs, namely the number of rows in y, as used in Eval(x, y). More...
 
void SetGradientSparsityPattern (const std::vector< std::pair< int, int >> &gradient_sparsity_pattern)
 Set the sparsity pattern of the gradient matrix ∂y/∂x (the gradient of y value in Eval, w.r.t x in Eval) . More...
 
const std::optional< std::vector< std::pair< int, int > > > & gradient_sparsity_pattern () const
 Returns the vector of (row_index, col_index) that contains all the entries in the gradient of Eval function (∂y/∂x) whose value could be non-zero, namely if ∂yᵢ/∂xⱼ could be non-zero, then the pair (i, j) is in gradient_sparsity_pattern. More...
 
 EvaluatorBase (const EvaluatorBase &)=delete
 
EvaluatorBaseoperator= (const EvaluatorBase &)=delete
 
 EvaluatorBase (EvaluatorBase &&)=delete
 
EvaluatorBaseoperator= (EvaluatorBase &&)=delete
 

Additional Inherited Members

- Protected Member Functions inherited from Constraint
void UpdateLowerBound (const Eigen::Ref< const Eigen::VectorXd > &new_lb)
 Updates the lower bound. More...
 
void UpdateUpperBound (const Eigen::Ref< const Eigen::VectorXd > &new_ub)
 Updates the upper bound. More...
 
void set_bounds (const Eigen::Ref< const Eigen::VectorXd > &lower_bound, const Eigen::Ref< const Eigen::VectorXd > &upper_bound)
 Set the upper and lower bounds of the constraint. More...
 
virtual bool DoCheckSatisfied (const Eigen::Ref< const Eigen::VectorXd > &x, const double tol) const
 
virtual bool DoCheckSatisfied (const Eigen::Ref< const AutoDiffVecXd > &x, const double tol) const
 
virtual symbolic::Formula DoCheckSatisfied (const Eigen::Ref< const VectorX< symbolic::Variable >> &x) const
 
- Protected Member Functions inherited from EvaluatorBase
 EvaluatorBase (int num_outputs, int num_vars, const std::string &description="")
 Constructs a evaluator. More...
 
virtual std::ostream & DoDisplay (std::ostream &os, const VectorX< symbolic::Variable > &vars) const
 NVI implementation of Display. More...
 
void set_num_outputs (int num_outputs)
 

Constructor & Destructor Documentation

◆ MinimumValueConstraint()

MinimumValueConstraint ( int  num_vars,
double  minimum_value,
double  influence_value_offset,
int  max_num_values,
std::function< AutoDiffVecXd(const Eigen::Ref< const AutoDiffVecXd > &, double)>  value_function 
)

Constructs a MinimumValueConstraint.

Parameters
num_varsThe number of inputs to value_function
minimum_valueThe minimum allowed value, vₘᵢₙ, for all elements of the vector returned by value_function.
influence_value_offsetThe difference between the influence value, v_influence, and the minimum value, vₘᵢₙ (see class documentation). This value must be finite and strictly positive, as it is used to scale the values returned by value_function. Smaller values may decrease the amount of computation required for each constraint evaluation if value_function can quickly determine that some elements will be larger than the influence value and skip the computation associated with those elements.
Default: 1
max_num_valuesThe maximum number of elements in the vector returned by value_function.
value_functionUser-provided function that takes a num_vars-element vector and the influence distance as inputs and returns a vector with up to max_num_values elements. The function can omit from the return vector any elements larger than the provided influence distance.
value_function_doubleOptional user-provide function that computes the same values as value_function but for double rather than AutoDiffXd. If omitted, value_function will be called (and the gradients discarded) when this constraint is evaluated for doubles.
Precondition
value_function_double(math::autoDiffToValueMatrix(x), v_influence) == math::autoDiffToValueMatrix(value_function(x, v_influence)) for all x.
value_function(x).size() <= max_num_values for all x.
Exceptions
std::invalid_argumentif influence_value_offset = ∞.
std::invalid_argumentif influence_value_offset ≤ 0.

◆ ~MinimumValueConstraint()

~MinimumValueConstraint ( )
override

Member Function Documentation

◆ influence_value()

double influence_value ( ) const

Getter for the influence value.

◆ max_num_values()

double max_num_values ( ) const

Getter for maximum number of values expected from value_function.

◆ minimum_value()

double minimum_value ( ) const

Getter for the minimum value.

◆ set_penalty_function()

void set_penalty_function ( MinimumValuePenaltyFunction  new_penalty_function)

Setter for the penalty function.


The documentation for this class was generated from the following file: